Open AccessA RDA-Based Deep Reinforcement Learning Approach for Autonomous Motion Planning of UAV in Dynamic Unknown Environments
Author(s) -
Kaifang Wan,
Xiaoguang Gao,
Zijian Hu,
Wei Zhang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1487/1/012006
Subject(s) - reinforcement learning , adaptability , planner , motion planning , scheme (mathematics) , computer science , motion (physics) , artificial intelligence , control (management) , real time computing , control engineering , engineering , robot , mathematics , ecology , mathematical analysis , biology
Autonomous motion planning (AMP) in dynamic unknown environments emerges as an urgent requirement with the prosperity of unmanned aerial vehicle (UAV). In this paper, we present a DRL-based planning framework to address the AMP problem, which is applicable in both military and civilian fields. To maintain learning efficiency, a novel reward difference amplifying (RDA) scheme is proposed to reshape the conventional reward functions and is introduced into state-of-the-art DRLs to constructs novel DRL algorithms for the planner’s learning. Different from conventional motion planning approaches, our DRL-based methods provide an end-to-end control for UAV, which directly maps the raw sensory measurements into high-level control signals. The training and testing experiments demonstrate that our RDA scheme makes great contributions to the performance improvement and provides the UAV good adaptability to dynamic environments.